Skip to content
#

full-reference-image-quality-assessment

Here are 4 public repositories matching this topic...

[ECCV 2022] We investigated a broad range of neural network elements and developed a robust perceptual similarity metric. Our shift-tolerant perceptual similarity metric (ST-LPIPS) is consistent with human perception and is less susceptible to imperceptible misalignments between two images than existing metrics.

  • Updated Oct 31, 2023
  • Python

[TMLR 2023] as a featured article (spotlight 🌟 or top 0.01% of the accepted papers). In this study, we systematically examine the robustness of both traditional and learned perceptual similarity metrics to imperceptible adversarial perturbations.

  • Updated May 18, 2023
  • Python

Improve this page

Add a description, image, and links to the full-reference-image-quality-assessment topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the full-reference-image-quality-assessment topic, visit your repo's landing page and select "manage topics."

Learn more